Data Governance structure: key enabler for Data Quality Management

Three prerequisites for the Financial Services Industry

An effective Data Governance structure is a key enabler in addressing Data Quality Management with respect to regulatory requirements in the Finance & Risk domain of the Financial Services Industry. However a number of prerequisites must be considered to ensure the successful embedding of a Data Governance structure within the entire organisation.

Data Governance structure as a key enabler

In recent years, the Financial Services Industry (FSI), predominantly banks and insurance companies, have suffered from the aftereffects of the financial crisis. While the FSI is more than willing to regain the customers’ trust, regulators on the other hand, impose stricter regulations in terms of requesting more detailed (granular) and more frequent Finance & Risk data in support of these regulatory requirements. For example the more traditional regulatory ‘report driven’ disclosures like CRDIV and Solvency II, but in addition also the more ‘data driven’ datasets like AnaCredit and the BCBS #239 Risk Principles with respect to effective data management. These regulations require the FSI to treat data as a crucial asset, not only from an internal management or commercial perspective, as any organisation should, but also from a regulatory perspective. Regulators, like the ECB for banks, are strengthening their influence over banks. This in order to have more control over and insight in the banks’ data, so that they are better equipped to swiftly react to fluctuations over capital and in particular liquidity positions when new economic downturns might occur. This more ‘data driven’ approach from regulators imposes a significant burden on the FSI; as the required data is not always available at the right granularity or doesn’t meet the required data quality standards.

This has led to an increased priority of Data Quality Management on executive board level. The FSI becomes more and more aware of the urgency of having complete, timely, adequate, accurate and granular data to support the data quality of datasets and regulatory reporting disclosures.

Most FSI companies are trying to adhere to these imposed data quality requirements and are indeed making some good progress in increasing the quality of data. However what we see nowadays is that Data Quality Management is particularly performed on an operational level and predominantly within a functional domain or business unit. The risk here is that solutions might be satisfying within a functional domain, but that they are contradictory from a group-level perspective, causing inefficiencies and sub-optimisation. The challenge at hand for the FSI is how they can ensure high quality data across the organisation.

To overcome this challenge, we consider it imperative to properly embed a Data Governance structure organisation-wide. This in order to have an overall steering mechanism in place which supports the improvement of Data Quality Management.

Therefore, a Data Governance structure should be implemented on different organisational levels, so that commonalities with respect to data quality issues are addressed across organisational boundaries. Hence, to create the awareness and adoption of roles and responsibilities on each level, i.e. on strategic, tactical and operational level, to ensure that improving data quality is an organisation-wide challenge.

Implementing a Data Governance structure, through a formalised governance mechanism such as a Data Governance Board, makes that Data Quality Management is integrally addressed based on a shared vision and structural solutions that can be rolled out for the entire organisation. The different levels also enhance shared decision-making and stewardship across organisational boundaries. In addition, the levels also enforce formalised sign-offs from Data Stewards[1] in support of both a ‘data and report driven’ approach between and within domains and business units.

Figure 1: A Data Governance structure contains several Data Quality Boards to tackle data quality issues from a group perspective

Three prerequisites to ensure the success

Having said that a Data Governance structure is crucial in addressing Data Quality Management across organisational boundaries, most FSI organisations, however, fail to actually implement such a governance successfully. To do so effectively, we recommend the following three prerequisites before even starting to implement a Data Governance structure.

1. Have both a ‘data and report driven’ mind-set throughout the entire organisation

Nowadays FSI organisations aim to be more and more ‘data driven’ while at the same time new requirements will continue to exist in the context of regulatory reports. To fully maintain an understanding of these regulatory reports and the impact on data requirements, any FSI organisation will have to ensure that all people throughout the organisation are involved with respect to the data flow (the flow of data from data origination to report). To be more precise, that ‘data driven’ mind-set and attitude with respect to Data Quality Management should start at the origination of data creation in the respective front and back-office systems up till the final regulatory report as requested by the data end-user. This mind-set of being both ‘data and report driven’ should be made concrete in a cross-organisationally shared vision, which better addresses Data Quality Management and eventually enhances the quality of data within the organisation to a higher level of maturity.

2. Business sponsorship on strategic, tactical and operational level is crucial to ensure that a Data Governance structure can act as an accelerator to formalise Data Quality Management

Since the Data Governance structure goes across organisational boundaries, Data Quality Management can be addressed at the right level and can be given priority. Therefore, we find it important to have business sponsorship on strategic, tactical and operational level within the different functional domains. Business has the responsibility to communicate new reporting requirements from the regulators directly to its entire organisation. We find that both Data Governance and Data Quality Management tend to have a symbiotic relationship, where one can only prosper with the existence of the other. From our experience, we often see that Data Governance is put on the agenda at strategic level because of Data Quality Management. The goal is then to use Data Governance as a means of improving Data Quality Management. We have seen several FSI organisations consciously and carefully considering how to cope with this symbiotic relationship with respect to the successful implementation of a Data Governance structure and Data Quality Management. From our point of view a successful outcome in addressing Data Quality Management can only be achieved when there is strong business sponsorship presence on strategic, tactical and operational level of the respective Data Quality Boards.

3. Without the proper tooling the data quality roles in the Data Governance structure do not come alive

Appropriate tooling in support of a Data Governance structure is paramount! As tooling not only accelerates the adoption and execution of the roles and responsibilities in Data Governance, it also makes these roles and responsibilities concrete and shows progress and results of data quality improvement. For instance, tooling that automatically updates a Data Steward on the requested improvement of a data quality issue, e.g. data definition improvements, makes the role of that Data Steward come alive. In turn this increases the acceptance and adoption of the Data Steward’s role. With the appropriate tooling, data quality issues and ownership of these issues are made visible, and even more importantly the progress and results of resolving data quality issues are made visible too, so that employees see the benefits of their effort. Finally, it enables data quality improvements on each organisational level, and it provides more transparency on how the different roles in the Data Governance structure are functioning.

Improvement of data quality and organisation responsiveness

When a Data Governance structure is implemented, formalised and acknowledged, FSI organisations have a detection and mitigation mechanism in place in which they can properly address data quality issues. This in order to have more control over and insight in the banks’ data, so that regulators can be given the needed guarantee for the quality of data provided through datasets or reports. But equally important is that it also increases organisations’ responsiveness to new business opportunities as strategic decisions are based on improved data. As such it will further improve the awareness that being both ‘data and report driven’ is the credo within the FSI for the years to come.

Consultant

Geoffrey has 3 years of experience as Consultant within Deloitte’s Enterprise Architecture Service Line. Geoffrey is part of the Enterprise Data Management team where he is considered an expert with r... Meer

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